Generative vs. Discriminative Recognition Models for Off-Line Arabic Handwriting

Sensors
Moftah Elzobi, Ayoub Al-Hamadi

Abstract

The majority of handwritten word recognition strategies are constructed on learning-based generative frameworks from letter or word training samples. Theoretically, constructing recognition models through discriminative learning should be the more effective alternative. The primary goal of this research is to compare the performances of discriminative and generative recognition strategies, which are described by generatively-trained hidden Markov modeling (HMM), discriminatively-trained conditional random fields (CRF) and discriminatively-trained hidden-state CRF (HCRF). With learning samples obtained from two dissimilar databases, we initially trained and applied an HMM classification scheme. To enable HMM classifiers to effectively reject incorrect and out-of-vocabulary segmentation, we enhance the models with adaptive threshold schemes. Aside from proposing such schemes for HMM classifiers, this research introduces CRF and HCRF classifiers in the recognition of offline Arabic handwritten words. Furthermore, the efficiencies of all three strategies are fully assessed using two dissimilar databases. Recognition outcomes for both words and letters are presented, with the pros and cons of each strategy emphasized.

References

Nov 16, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Greg MoriJitendra Malik
Apr 28, 2006·IEEE Transactions on Pattern Analysis and Machine Intelligence·Liana M Lorigo, Venu Govindaraju
May 16, 2009·IEEE Transactions on Pattern Analysis and Machine Intelligence·Ramy Al-Hajj MohamadChafic Mokbel
Aug 24, 2013·IEEE Transactions on Pattern Analysis and Machine Intelligence·Xiang-Dong ZhouMasaki Nakagawa

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Citations

Nov 20, 2020·Sensors·Xin Zhang, Yang Xue

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Methods Mentioned

BETA
feature extraction

Software Mentioned

Google spell checker
HCRF
Windows
MATLAB HMM
MS checker
MATLAB
Hunspell

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